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    Comparison of control algorithms for the blood glucose concentration in a virtual patient with an artificial pancreas
    (Elsevier, 2012) Semizer, E.; Yuceer, M.; Atasoy, I.; Berber, R.
    To obtain the most suitable control algorithm for a wearable artificial pancreas, different control algorithms were compared and tested using a Hovorka model. Model predictive control (MPC), linear and nonlinear model forms, proportional integral derivative control (PID), neural-network-based model predictive control (NN-MPC), nonlinear autoregressive moving average (NARMA-L2) and sequential quadratic programming (SQP) were evaluated using the Hovorka model. Due to the fact that modeling of biomedical processes are very complex, to present the most effective control algorithm, various control strategies were needed to application. In the control algorithms, set point tracking and disturbance rejection were performed. With respect to the rise times of the control algorithms, SQP with optimal control had the shortest time, and NARMA-L2 had the longest time. Because the control algorithm connects the glucose meter and the insulin pump in an artificial pancreas, the rise time is the most important parameter. We propose that optimal control with SQP is the most suitable control algorithm to connect the glucose meter and the insulin pump. (C) 2011 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
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    AN INTERACTIVE GIS-BASED SOFTWARE FOR DYNAMIC MONITORING OF RIVERS
    (Scibulcom Ltd, 2014) Yetik, M. K.; Yuceer, M.; Karadurmus, E.; Semizer, E.; Calimli, A.; Berber, R.
    Water quality research and development attempts have been the most valuable resources in the sense of model calibration and verification techniques. Due to the fact that current degree of pollution in rivers and importance of the sustainable water resources management, the interactive river monitoring becomes inevitable. Within the scope of river water quality monitoring, Geographical Information Systems (GIS) are gaining widespread acceptance besides this fast and reliable water quality models and parameter estimation techniques are becoming available. However, integrating water quality models with GIS is limited in literature. This study presents an integrated platform on which ArcMap as a GIS and a water quality model in MATLAB are brought together in an interactive and user friendly manner. The software provides a considerable developments in future real time river monitoring and environmental pollution assessment.
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    A parameter identifiability and estimation study in Yesilirmak River
    (Iwa Publishing, 2009) Berber, R.; Yuceer, M.; Karadurmus, E.
    Water quality models have relatively large number of parameters, which need to be estimated against observed data through a non-trivial task that is associated with substantial difficulties. This work involves a systematic model calibration and validation study for river water quality. The model considered was composed of dynamic mass balances for eleven pollution constituents, stemming from QUAL2E water quality model by considering a river segment as a series of continuous stirred-tank reactors (CSTRs). Parameter identifiability was analyzed from the perspective of sensitivity measure and collinearity index, which indicated that 8 parameters would fall within the identifiability range. The model parameters were then estimated by an integration based optimization algorithm coupled with sequential quadratic programming. Dynamic field data consisting of major pollutant concentrations were collected from sampling stations along Yesilirmak River around the city of Amasya in Turkey, and compared with model predictions. The calibrated model responses were in good agreement with the observed river water quality data, and this indicated that the suggested procedure provided an effective means for reliable estimation of model parameters and dynamic simulation for river streams.
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    A SOFTWARE FOR PARAMETER ESTIMATION IN DYNAMIC MODELS
    (Brazilian Soc Chemical Eng, 2008) Yuceer, M.; Atasoy, I.; Berber, R.
    A common problem in dynamic systems is to determine parameters in an equation used to represent experimental data. The goal is to determine the values of model parameters that provide the best fit to measured data, generally based on some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently non-convex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. A user-interactive parameter estimation software was needed for identifying kinetic parameters. In this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES) has been developed in MATLAB environment. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.